Overview

Dataset statistics

Number of variables17
Number of observations38014
Missing cells37097
Missing cells (%)5.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.9 MiB
Average record size in memory136.0 B

Variable types

Categorical3
Numeric14

Alerts

HolidayFlag is highly imbalanced (75.6%)Imbalance
Holiday has 36478 (96.0%) missing valuesMissing
DayOfWeek has 5424 (14.3%) zerosZeros
PeriodOfDay has 792 (2.1%) zerosZeros

Reproduction

Analysis started2024-07-19 00:58:41.380189
Analysis finished2024-07-19 00:59:38.746788
Duration57.37 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

Holiday
Categorical

MISSING 

Distinct14
Distinct (%)0.9%
Missing36478
Missing (%)96.0%
Memory size297.1 KiB
Christmas Eve
144 
Christmas
144 
St Stephen's Day
144 
New Year's Eve
144 
New Year's Day
96 
Other values (9)
864 

Length

Max length20
Median length17
Mean length13.375
Min length6

Characters and Unicode

Total characters20544
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowChristmas Eve
2nd rowChristmas Eve
3rd rowChristmas Eve
4th rowChristmas Eve
5th rowChristmas Eve

Common Values

ValueCountFrequency (%)
Christmas Eve 144
 
0.4%
Christmas 144
 
0.4%
St Stephen's Day 144
 
0.4%
New Year's Eve 144
 
0.4%
New Year's Day 96
 
0.3%
St Patrick's Day 96
 
0.3%
Good Friday 96
 
0.3%
Holy Saturday 96
 
0.3%
Easter 96
 
0.3%
Easter Monday 96
 
0.3%
Other values (4) 384
 
1.0%
(Missing) 36478
96.0%

Length

2024-07-18T20:59:43.326622image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
day 432
12.0%
christmas 288
 
8.0%
holiday 288
 
8.0%
bank 288
 
8.0%
eve 288
 
8.0%
st 240
 
6.7%
new 240
 
6.7%
year's 240
 
6.7%
easter 192
 
5.3%
stephen's 144
 
4.0%
Other values (10) 960
26.7%

Most occurring characters

ValueCountFrequency (%)
a 2304
 
11.2%
2064
 
10.0%
e 1440
 
7.0%
s 1344
 
6.5%
t 1248
 
6.1%
y 1200
 
5.8%
r 1104
 
5.4%
o 768
 
3.7%
i 768
 
3.7%
d 672
 
3.3%
Other values (28) 7632
37.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20544
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2304
 
11.2%
2064
 
10.0%
e 1440
 
7.0%
s 1344
 
6.5%
t 1248
 
6.1%
y 1200
 
5.8%
r 1104
 
5.4%
o 768
 
3.7%
i 768
 
3.7%
d 672
 
3.3%
Other values (28) 7632
37.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20544
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2304
 
11.2%
2064
 
10.0%
e 1440
 
7.0%
s 1344
 
6.5%
t 1248
 
6.1%
y 1200
 
5.8%
r 1104
 
5.4%
o 768
 
3.7%
i 768
 
3.7%
d 672
 
3.3%
Other values (28) 7632
37.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20544
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2304
 
11.2%
2064
 
10.0%
e 1440
 
7.0%
s 1344
 
6.5%
t 1248
 
6.1%
y 1200
 
5.8%
r 1104
 
5.4%
o 768
 
3.7%
i 768
 
3.7%
d 672
 
3.3%
Other values (28) 7632
37.1%

HolidayFlag
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size297.1 KiB
0
36478 
1
 
1536

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters38014
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 36478
96.0%
1 1536
 
4.0%

Length

2024-07-18T20:59:43.931529image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-18T20:59:44.218486image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 36478
96.0%
1 1536
 
4.0%

Most occurring characters

ValueCountFrequency (%)
0 36478
96.0%
1 1536
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 38014
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36478
96.0%
1 1536
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 38014
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36478
96.0%
1 1536
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 38014
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36478
96.0%
1 1536
 
4.0%

DayOfWeek
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9973168
Minimum0
Maximum6
Zeros5424
Zeros (%)14.3%
Negative0
Negative (%)0.0%
Memory size297.1 KiB
2024-07-18T20:59:44.532801image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.9999587
Coefficient of variation (CV)0.66724971
Kurtosis-1.2508886
Mean2.9973168
Median Absolute Deviation (MAD)2
Skewness0.0025846685
Sum113940
Variance3.999835
MonotonicityNot monotonic
2024-07-18T20:59:44.688582image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 5472
14.4%
2 5424
14.3%
3 5424
14.3%
4 5424
14.3%
5 5424
14.3%
0 5424
14.3%
6 5422
14.3%
ValueCountFrequency (%)
0 5424
14.3%
1 5472
14.4%
2 5424
14.3%
3 5424
14.3%
4 5424
14.3%
5 5424
14.3%
6 5422
14.3%
ValueCountFrequency (%)
6 5422
14.3%
5 5424
14.3%
4 5424
14.3%
3 5424
14.3%
2 5424
14.3%
1 5472
14.4%
0 5424
14.3%

WeekOfYear
Real number (ℝ)

Distinct52
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.124586
Minimum1
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size297.1 KiB
2024-07-18T20:59:44.950445image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q115
median29
Q343
95-th percentile51
Maximum52
Range51
Interquartile range (IQR)28

Descriptive statistics

Standard deviation15.587575
Coefficient of variation (CV)0.55423307
Kurtosis-1.2695024
Mean28.124586
Median Absolute Deviation (MAD)14
Skewness-0.10882505
Sum1069128
Variance242.97251
MonotonicityNot monotonic
2024-07-18T20:59:45.200200image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45 1008
 
2.7%
46 1008
 
2.7%
47 1008
 
2.7%
48 1008
 
2.7%
49 1008
 
2.7%
50 1008
 
2.7%
51 1008
 
2.7%
52 1008
 
2.7%
44 960
 
2.5%
1 768
 
2.0%
Other values (42) 28222
74.2%
ValueCountFrequency (%)
1 768
2.0%
2 672
1.8%
3 672
1.8%
4 672
1.8%
5 672
1.8%
6 672
1.8%
7 672
1.8%
8 672
1.8%
9 672
1.8%
10 672
1.8%
ValueCountFrequency (%)
52 1008
2.7%
51 1008
2.7%
50 1008
2.7%
49 1008
2.7%
48 1008
2.7%
47 1008
2.7%
46 1008
2.7%
45 1008
2.7%
44 960
2.5%
43 672
1.8%

Day
Real number (ℝ)

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.739412
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size297.1 KiB
2024-07-18T20:59:45.459715image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8042468
Coefficient of variation (CV)0.55937584
Kurtosis-1.1951458
Mean15.739412
Median Absolute Deviation (MAD)8
Skewness0.0058539775
Sum598318
Variance77.514761
MonotonicityNot monotonic
2024-07-18T20:59:45.633056image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 1248
 
3.3%
15 1248
 
3.3%
28 1248
 
3.3%
27 1248
 
3.3%
26 1248
 
3.3%
24 1248
 
3.3%
23 1248
 
3.3%
22 1248
 
3.3%
21 1248
 
3.3%
20 1248
 
3.3%
Other values (21) 25534
67.2%
ValueCountFrequency (%)
1 1248
3.3%
2 1248
3.3%
3 1248
3.3%
4 1248
3.3%
5 1248
3.3%
6 1248
3.3%
7 1248
3.3%
8 1248
3.3%
9 1248
3.3%
10 1248
3.3%
ValueCountFrequency (%)
31 720
1.9%
30 1152
3.0%
29 1200
3.2%
28 1248
3.3%
27 1248
3.3%
26 1248
3.3%
25 1246
3.3%
24 1248
3.3%
23 1248
3.3%
22 1248
3.3%

Month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9042458
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size297.1 KiB
2024-07-18T20:59:45.781739image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.5736957
Coefficient of variation (CV)0.51760841
Kurtosis-1.2750042
Mean6.9042458
Median Absolute Deviation (MAD)3
Skewness-0.114857
Sum262458
Variance12.771301
MonotonicityNot monotonic
2024-07-18T20:59:46.038287image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
12 4464
11.7%
11 4320
11.4%
1 2976
7.8%
5 2976
7.8%
7 2976
7.8%
8 2976
7.8%
10 2976
7.8%
3 2974
7.8%
4 2880
7.6%
6 2880
7.6%
Other values (2) 5616
14.8%
ValueCountFrequency (%)
1 2976
7.8%
2 2736
7.2%
3 2974
7.8%
4 2880
7.6%
5 2976
7.8%
6 2880
7.6%
7 2976
7.8%
8 2976
7.8%
9 2880
7.6%
10 2976
7.8%
ValueCountFrequency (%)
12 4464
11.7%
11 4320
11.4%
10 2976
7.8%
9 2880
7.6%
8 2976
7.8%
7 2976
7.8%
6 2880
7.6%
5 2976
7.8%
4 2880
7.6%
3 2974
7.8%

Year
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size297.1 KiB
2012
17566 
2013
17520 
2011
2928 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters152056
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2011
2nd row2011
3rd row2011
4th row2011
5th row2011

Common Values

ValueCountFrequency (%)
2012 17566
46.2%
2013 17520
46.1%
2011 2928
 
7.7%

Length

2024-07-18T20:59:46.187714image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-18T20:59:46.292970image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2012 17566
46.2%
2013 17520
46.1%
2011 2928
 
7.7%

Most occurring characters

ValueCountFrequency (%)
2 55580
36.6%
1 40942
26.9%
0 38014
25.0%
3 17520
 
11.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 152056
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 55580
36.6%
1 40942
26.9%
0 38014
25.0%
3 17520
 
11.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 152056
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 55580
36.6%
1 40942
26.9%
0 38014
25.0%
3 17520
 
11.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 152056
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 55580
36.6%
1 40942
26.9%
0 38014
25.0%
3 17520
 
11.5%

PeriodOfDay
Real number (ℝ)

ZEROS 

Distinct48
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.501105
Minimum0
Maximum47
Zeros792
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size297.1 KiB
2024-07-18T20:59:46.629278image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q112
median24
Q335.75
95-th percentile45
Maximum47
Range47
Interquartile range (IQR)23.75

Descriptive statistics

Standard deviation13.853108
Coefficient of variation (CV)0.58946624
Kurtosis-1.2009804
Mean23.501105
Median Absolute Deviation (MAD)12
Skewness-5.5676968 × 10-5
Sum893371
Variance191.9086
MonotonicityNot monotonic
2024-07-18T20:59:46.855941image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0 792
 
2.1%
1 792
 
2.1%
26 792
 
2.1%
27 792
 
2.1%
28 792
 
2.1%
29 792
 
2.1%
30 792
 
2.1%
31 792
 
2.1%
32 792
 
2.1%
33 792
 
2.1%
Other values (38) 30094
79.2%
ValueCountFrequency (%)
0 792
2.1%
1 792
2.1%
2 791
2.1%
3 791
2.1%
4 792
2.1%
5 792
2.1%
6 792
2.1%
7 792
2.1%
8 792
2.1%
9 792
2.1%
ValueCountFrequency (%)
47 792
2.1%
46 792
2.1%
45 792
2.1%
44 792
2.1%
43 792
2.1%
42 792
2.1%
41 792
2.1%
40 792
2.1%
39 792
2.1%
38 792
2.1%

ForecastWindProduction
Real number (ℝ)

Distinct27475
Distinct (%)72.3%
Missing5
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean544.26145
Minimum0.68
Maximum1680
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size297.1 KiB
2024-07-18T20:59:47.084933image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.68
5-th percentile52.322
Q1189.67
median441.98
Q3839.46
95-th percentile1352.44
Maximum1680
Range1679.32
Interquartile range (IQR)649.79

Descriptive statistics

Standard deviation414.36463
Coefficient of variation (CV)0.76133378
Kurtosis-0.6826475
Mean544.26145
Median Absolute Deviation (MAD)294.42
Skewness0.6599677
Sum20686833
Variance171698.05
MonotonicityNot monotonic
2024-07-18T20:59:47.215213image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54.1 10
 
< 0.1%
61.1 10
 
< 0.1%
129.9 9
 
< 0.1%
180.8 8
 
< 0.1%
169.9 8
 
< 0.1%
245.4 8
 
< 0.1%
287.8 8
 
< 0.1%
84.7 8
 
< 0.1%
68.7 8
 
< 0.1%
77.6 8
 
< 0.1%
Other values (27465) 37924
99.8%
ValueCountFrequency (%)
0.68 1
< 0.1%
0.69 1
< 0.1%
0.77 1
< 0.1%
0.86 1
< 0.1%
0.9 1
< 0.1%
1.11 1
< 0.1%
1.19 1
< 0.1%
1.63 1
< 0.1%
1.78 1
< 0.1%
1.79 1
< 0.1%
ValueCountFrequency (%)
1680 1
< 0.1%
1673.8 1
< 0.1%
1672.5 1
< 0.1%
1666.4 1
< 0.1%
1659.6 1
< 0.1%
1655.5 1
< 0.1%
1654.8 1
< 0.1%
1653.3 1
< 0.1%
1644.9 1
< 0.1%
1638.8 1
< 0.1%

SystemLoadEA
Real number (ℝ)

Distinct35584
Distinct (%)93.6%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean4020.085
Minimum2183.94
Maximum6492.91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size297.1 KiB
2024-07-18T20:59:47.412033image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2183.94
5-th percentile2626.2495
Q13281.2075
median4103.6
Q34638.5325
95-th percentile5367.076
Maximum6492.91
Range4308.97
Interquartile range (IQR)1357.325

Descriptive statistics

Standard deviation860.47687
Coefficient of variation (CV)0.21404444
Kurtosis-0.81026336
Mean4020.085
Median Absolute Deviation (MAD)655.115
Skewness-0.0082827309
Sum1.5281147 × 108
Variance740420.44
MonotonicityNot monotonic
2024-07-18T20:59:47.684363image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2969.18 4
 
< 0.1%
4241.05 4
 
< 0.1%
2953.35 4
 
< 0.1%
4018.34 4
 
< 0.1%
4503.32 3
 
< 0.1%
4668.29 3
 
< 0.1%
4532.07 3
 
< 0.1%
3879.02 3
 
< 0.1%
2986.59 3
 
< 0.1%
4414.07 3
 
< 0.1%
Other values (35574) 37978
99.9%
ValueCountFrequency (%)
2183.94 1
< 0.1%
2210.42 1
< 0.1%
2212.39 1
< 0.1%
2213.92 1
< 0.1%
2234.79 1
< 0.1%
2235.11 1
< 0.1%
2238.68 1
< 0.1%
2247.23 1
< 0.1%
2248.05 1
< 0.1%
2248.57 1
< 0.1%
ValueCountFrequency (%)
6492.91 1
< 0.1%
6473.69 1
< 0.1%
6463.94 1
< 0.1%
6444.79 1
< 0.1%
6431 1
< 0.1%
6420.6 1
< 0.1%
6414.1 1
< 0.1%
6412.47 1
< 0.1%
6407.95 1
< 0.1%
6397.47 1
< 0.1%

SMPEA
Real number (ℝ)

Distinct7339
Distinct (%)19.3%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean62.720388
Minimum0
Maximum587.58
Zeros15
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size297.1 KiB
2024-07-18T20:59:47.921265image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33.82
Q145.53
median55.23
Q370.32
95-th percentile110.229
Maximum587.58
Range587.58
Interquartile range (IQR)24.79

Descriptive statistics

Standard deviation32.252334
Coefficient of variation (CV)0.51422409
Kurtosis34.329032
Mean62.720388
Median Absolute Deviation (MAD)11.5
Skewness4.3990847
Sum2384127.4
Variance1040.2131
MonotonicityNot monotonic
2024-07-18T20:59:48.055710image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49.56 63
 
0.2%
55.72 44
 
0.1%
66.74 42
 
0.1%
47.33 42
 
0.1%
48 42
 
0.1%
50.24 41
 
0.1%
51.89 39
 
0.1%
75.5 39
 
0.1%
52.85 38
 
0.1%
44.91 38
 
0.1%
Other values (7329) 37584
98.9%
ValueCountFrequency (%)
0 15
< 0.1%
0.01 1
 
< 0.1%
2.53 2
 
< 0.1%
3.72 1
 
< 0.1%
6.02 6
 
< 0.1%
6.07 5
 
< 0.1%
10 2
 
< 0.1%
11.31 1
 
< 0.1%
13.69 3
 
< 0.1%
13.7 4
 
< 0.1%
ValueCountFrequency (%)
587.58 1
< 0.1%
563.06 1
< 0.1%
525.95 1
< 0.1%
484.52 1
< 0.1%
484.12 1
< 0.1%
483.25 1
< 0.1%
482.44 1
< 0.1%
478.79 1
< 0.1%
474.44 1
< 0.1%
474.39 1
< 0.1%

ORKTemperature
Real number (ℝ)

Distinct30
Distinct (%)0.1%
Missing295
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean9.6263687
Minimum-4
Maximum25
Zeros214
Zeros (%)0.6%
Negative149
Negative (%)0.4%
Memory size297.1 KiB
2024-07-18T20:59:48.281757image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-4
5-th percentile3
Q16
median9
Q313
95-th percentile17
Maximum25
Range29
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.4399339
Coefficient of variation (CV)0.46122624
Kurtosis-0.24263331
Mean9.6263687
Median Absolute Deviation (MAD)3
Skewness0.18538177
Sum363097
Variance19.713013
MonotonicityNot monotonic
2024-07-18T20:59:48.618759image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
9 3525
 
9.3%
10 3230
 
8.5%
8 3225
 
8.5%
11 3017
 
7.9%
7 2894
 
7.6%
12 2712
 
7.1%
6 2617
 
6.9%
5 2027
 
5.3%
13 2009
 
5.3%
15 1883
 
5.0%
Other values (20) 10580
27.8%
ValueCountFrequency (%)
-4 2
 
< 0.1%
-3 14
 
< 0.1%
-2 30
 
0.1%
-1 103
 
0.3%
0 214
 
0.6%
1 531
 
1.4%
2 953
2.5%
3 1399
3.7%
4 1580
4.2%
5 2027
5.3%
ValueCountFrequency (%)
25 13
 
< 0.1%
24 43
 
0.1%
23 64
 
0.2%
22 75
 
0.2%
21 135
 
0.4%
20 195
 
0.5%
19 330
 
0.9%
18 592
1.6%
17 1001
2.6%
16 1451
3.8%

ORKWindspeed
Real number (ℝ)

Distinct52
Distinct (%)0.1%
Missing299
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean19.21177
Minimum0
Maximum75.9
Zeros157
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size297.1 KiB
2024-07-18T20:59:49.000867image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.6
Q113
median18.5
Q324.1
95-th percentile37
Maximum75.9
Range75.9
Interquartile range (IQR)11.1

Descriptive statistics

Standard deviation9.571311
Coefficient of variation (CV)0.49820038
Kurtosis0.68175567
Mean19.21177
Median Absolute Deviation (MAD)5.6
Skewness0.76934671
Sum724571.9
Variance91.609994
MonotonicityNot monotonic
2024-07-18T20:59:49.145665image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.8 3208
 
8.4%
13 3143
 
8.3%
16.7 3015
 
7.9%
18.5 2827
 
7.4%
11.1 2823
 
7.4%
20.4 2584
 
6.8%
9.3 2467
 
6.5%
22.2 2249
 
5.9%
24.1 2116
 
5.6%
25.9 1825
 
4.8%
Other values (42) 11458
30.1%
ValueCountFrequency (%)
0 157
 
0.4%
1.9 222
 
0.6%
3.6 2
 
< 0.1%
3.7 583
 
1.5%
5.6 1181
3.1%
7.2 1
 
< 0.1%
7.4 1760
4.6%
9.3 2467
6.5%
10.8 13
 
< 0.1%
11.1 2823
7.4%
ValueCountFrequency (%)
75.9 1
 
< 0.1%
74.1 1
 
< 0.1%
72.2 1
 
< 0.1%
70.4 2
 
< 0.1%
68.5 2
 
< 0.1%
66.7 3
< 0.1%
64.8 6
< 0.1%
63 6
< 0.1%
61.1 6
< 0.1%
59.3 7
< 0.1%

CO2Intensity
Real number (ℝ)

Distinct22458
Distinct (%)59.1%
Missing7
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean479.37304
Minimum0
Maximum842.88
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size297.1 KiB
2024-07-18T20:59:49.419493image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile336.093
Q1421.105
median480.31
Q3537.52
95-th percentile619.191
Maximum842.88
Range842.88
Interquartile range (IQR)116.415

Descriptive statistics

Standard deviation85.354706
Coefficient of variation (CV)0.17805487
Kurtosis-0.11223939
Mean479.37304
Median Absolute Deviation (MAD)58.26
Skewness0.0099313035
Sum18219531
Variance7285.4259
MonotonicityNot monotonic
2024-07-18T20:59:49.557254image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
473.92 9
 
< 0.1%
491.32 9
 
< 0.1%
493.1 9
 
< 0.1%
482.39 8
 
< 0.1%
474.49 8
 
< 0.1%
535.7 8
 
< 0.1%
468.67 8
 
< 0.1%
508.02 8
 
< 0.1%
476.76 8
 
< 0.1%
475.3 8
 
< 0.1%
Other values (22448) 37924
99.8%
ValueCountFrequency (%)
0 1
< 0.1%
29.41 1
< 0.1%
202.2 1
< 0.1%
203.27 1
< 0.1%
203.46 1
< 0.1%
203.79 1
< 0.1%
206.21 1
< 0.1%
206.58 1
< 0.1%
208.21 1
< 0.1%
209.61 1
< 0.1%
ValueCountFrequency (%)
842.88 1
< 0.1%
836.78 1
< 0.1%
816.85 1
< 0.1%
814.61 1
< 0.1%
804.35 1
< 0.1%
798.54 1
< 0.1%
794.16 1
< 0.1%
792.5 1
< 0.1%
791.08 1
< 0.1%
789.91 1
< 0.1%

ActualWindProduction
Real number (ℝ)

Distinct1535
Distinct (%)4.0%
Missing5
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean520.76282
Minimum1
Maximum1769
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size297.1 KiB
2024-07-18T20:59:49.831915image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile43
Q1199
median445
Q3793
95-th percentile1243
Maximum1769
Range1768
Interquartile range (IQR)594

Descriptive statistics

Standard deviation378.28298
Coefficient of variation (CV)0.72640166
Kurtosis-0.64553791
Mean520.76282
Median Absolute Deviation (MAD)282
Skewness0.59657528
Sum19793674
Variance143098.01
MonotonicityNot monotonic
2024-07-18T20:59:50.034523image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54 75
 
0.2%
46 73
 
0.2%
58 72
 
0.2%
53 71
 
0.2%
48 70
 
0.2%
23 67
 
0.2%
45 67
 
0.2%
61 67
 
0.2%
42 66
 
0.2%
52 66
 
0.2%
Other values (1525) 37315
98.2%
ValueCountFrequency (%)
1 3
 
< 0.1%
2 2
 
< 0.1%
3 5
 
< 0.1%
4 7
 
< 0.1%
5 12
 
< 0.1%
6 28
0.1%
7 31
0.1%
8 39
0.1%
9 42
0.1%
10 26
0.1%
ValueCountFrequency (%)
1769 1
< 0.1%
1760 1
< 0.1%
1742 1
< 0.1%
1738 1
< 0.1%
1705 1
< 0.1%
1630 1
< 0.1%
1625 1
< 0.1%
1624 1
< 0.1%
1606 1
< 0.1%
1598 1
< 0.1%

SystemLoadEP2
Real number (ℝ)

Distinct35653
Distinct (%)93.8%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean3785.9738
Minimum1809.96
Maximum6309.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size297.1 KiB
2024-07-18T20:59:50.372957image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1809.96
5-th percentile2449.541
Q13058.2775
median3865.745
Q34427.59
95-th percentile5087.543
Maximum6309.75
Range4499.79
Interquartile range (IQR)1369.3125

Descriptive statistics

Standard deviation843.26946
Coefficient of variation (CV)0.22273515
Kurtosis-0.86737217
Mean3785.9738
Median Absolute Deviation (MAD)651.035
Skewness-0.030069417
Sum1.4391244 × 108
Variance711103.37
MonotonicityNot monotonic
2024-07-18T20:59:50.547241image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4426.84 4
 
< 0.1%
4430.63 4
 
< 0.1%
4507.51 4
 
< 0.1%
4401.65 3
 
< 0.1%
3711.78 3
 
< 0.1%
3092.11 3
 
< 0.1%
4595.84 3
 
< 0.1%
4490.01 3
 
< 0.1%
4296.1 3
 
< 0.1%
4585.57 3
 
< 0.1%
Other values (35643) 37979
99.9%
ValueCountFrequency (%)
1809.96 1
< 0.1%
1823.45 1
< 0.1%
1832.68 1
< 0.1%
1848.62 1
< 0.1%
1853.34 1
< 0.1%
1855.41 1
< 0.1%
1855.91 1
< 0.1%
1860.52 1
< 0.1%
1861.48 1
< 0.1%
1867.3 1
< 0.1%
ValueCountFrequency (%)
6309.75 1
< 0.1%
6291.31 1
< 0.1%
6198.69 1
< 0.1%
6183.15 1
< 0.1%
6165.66 1
< 0.1%
6128.27 1
< 0.1%
6120.54 1
< 0.1%
6070.86 1
< 0.1%
6065.66 1
< 0.1%
6065.24 1
< 0.1%

SMPEP2
Real number (ℝ)

Distinct7813
Distinct (%)20.6%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean64.136823
Minimum-47.74
Maximum1000
Zeros23
Zeros (%)0.1%
Negative3
Negative (%)< 0.1%
Memory size297.1 KiB
2024-07-18T20:59:51.088667image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-47.74
5-th percentile33.19
Q145.78
median55.545
Q372.11
95-th percentile118.6345
Maximum1000
Range1047.74
Interquartile range (IQR)26.33

Descriptive statistics

Standard deviation35.415036
Coefficient of variation (CV)0.55217946
Kurtosis52.88416
Mean64.136823
Median Absolute Deviation (MAD)12.055
Skewness5.0364369
Sum2437968.9
Variance1254.2248
MonotonicityNot monotonic
2024-07-18T20:59:57.847935image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42.04 55
 
0.1%
44.98 51
 
0.1%
49.89 51
 
0.1%
44.1 51
 
0.1%
75.71 51
 
0.1%
44.52 48
 
0.1%
53.6 40
 
0.1%
47.44 39
 
0.1%
49.76 38
 
0.1%
49.56 38
 
0.1%
Other values (7803) 37550
98.8%
ValueCountFrequency (%)
-47.74 1
 
< 0.1%
-3.5 2
 
< 0.1%
0 23
0.1%
0.03 1
 
< 0.1%
1.07 5
 
< 0.1%
1.77 2
 
< 0.1%
5.03 4
 
< 0.1%
5.35 3
 
< 0.1%
5.52 1
 
< 0.1%
5.66 6
 
< 0.1%
ValueCountFrequency (%)
1000 1
< 0.1%
699.49 1
< 0.1%
682.85 1
< 0.1%
675.58 1
< 0.1%
657.08 1
< 0.1%
630.19 1
< 0.1%
603 1
< 0.1%
590.74 1
< 0.1%
517.69 1
< 0.1%
517.4 1
< 0.1%

Interactions

2024-07-18T20:59:31.416904image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:45.844313image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:48.714587image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:55.721342image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:57.982349image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:00.328465image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:03.213513image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:06.247790image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:08.537485image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:13.226465image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:19.328368image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:24.476598image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:27.100209image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:29.100344image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:31.575084image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:46.164053image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:53.264119image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:55.876837image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:58.137925image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:00.480692image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:03.404233image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:06.425777image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:08.685618image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:13.670668image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:19.840342image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:24.648737image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:27.259465image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:29.238318image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:31.731192image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:46.333264image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:53.445358image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:56.031072image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:58.280725image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:00.671124image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:03.587952image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:06.588744image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:08.811931image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:14.178751image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:20.189887image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:24.819936image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:27.431276image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:29.404150image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:31.909792image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:46.496304image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:53.658293image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:56.190188image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:58.426908image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:00.857421image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:03.794493image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:06.734703image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:08.942664image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:14.678667image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:20.572775image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:25.002581image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:27.557881image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:29.578263image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:32.070353image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:46.678594image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:53.860554image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:56.375947image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:58.590450image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:01.019594image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:04.008206image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:06.871233image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:09.072741image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:15.093949image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:20.951459image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:25.135354image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:27.696530image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:29.750388image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:32.207052image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:46.862329image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:54.012934image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:56.522631image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:58.770182image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:01.221355image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:04.196329image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:07.011930image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:09.429607image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:15.390933image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:21.324839image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:25.266488image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:27.824850image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:29.923729image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:32.348471image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:47.088061image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:54.233746image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:56.711891image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:58.997369image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:01.408464image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:04.429802image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:07.200455image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:09.848552image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:15.919827image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:21.771083image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:25.860260image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:27.960368image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:30.100381image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:32.501183image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:47.308586image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:54.405223image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:56.881794image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:59.159856image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:01.670594image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:04.629326image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:07.370529image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:10.158404image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:16.103883image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:22.206546image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:26.021514image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:28.077137image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:30.238693image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:32.686566image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:47.514008image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:54.605557image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:57.046566image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:59.327393image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:01.925195image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:04.818224image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:07.520650image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:10.497219image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:16.220651image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:22.701590image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:26.174236image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:28.237849image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:30.444245image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:32.844796image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:47.750739image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:54.813694image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:57.203007image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:59.513974image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:02.170053image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:05.008836image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:07.685810image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:10.870021image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:16.750076image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:23.110087image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:26.307636image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:28.378500image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:30.633842image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:33.010169image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:47.968746image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:55.028208image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:57.356997image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:59.688227image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:02.400774image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:05.563237image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:07.863029image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:11.323459image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:17.247247image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:23.618222image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:26.475904image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:28.513434image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:30.833423image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:33.141664image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:48.140594image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:55.196949image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:57.509641image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:59.837018image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:02.567877image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:05.740222image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:08.041861image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:11.729917image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:17.743832image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:23.890252image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:26.627764image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:28.652160image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:31.001529image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:33.290043image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:48.326437image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:55.375565image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:57.674248image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:00.000202image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:02.769433image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:05.924786image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:08.207264image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:12.234441image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:18.370737image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:24.100891image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:26.833833image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:28.806961image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:31.138497image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:33.427344image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:48.504496image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:55.553578image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:58:57.813720image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:00.175253image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:03.007533image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:06.093253image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:08.352305image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:12.793880image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:18.886907image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:24.280426image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:26.963800image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:28.952052image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-18T20:59:31.274929image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-07-18T20:59:33.728511image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-18T20:59:34.489304image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-07-18T20:59:35.772656image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

HolidayHolidayFlagDayOfWeekWeekOfYearDayMonthYearPeriodOfDayForecastWindProductionSystemLoadEASMPEAORKTemperatureORKWindspeedCO2IntensityActualWindProductionSystemLoadEP2SMPEP2
0None014411120110315.313388.7749.266.09.3600.71356.03159.6054.32
1None014411120111321.803196.6649.266.011.1605.42317.02973.0154.23
2None014411120112328.573060.7149.105.011.1589.97311.02834.0054.23
3None014411120113335.602945.5648.046.09.3585.94313.02725.9953.47
4None014411120114342.902849.3433.756.011.1571.52346.02655.6439.87
5None014411120115342.972810.0133.755.011.1562.61342.02585.9939.87
6None014411120116343.182780.5233.755.07.4545.81336.02561.7039.87
7None014411120117343.462762.6733.755.09.3539.38338.02544.3339.87
8None014411120118343.882766.6333.754.011.1538.70347.02549.0239.87
9None014411120119344.392786.8033.754.07.4540.39338.02547.1539.87
HolidayHolidayFlagDayOfWeekWeekOfYearDayMonthYearPeriodOfDayForecastWindProductionSystemLoadEASMPEAORKTemperatureORKWindspeedCO2IntensityActualWindProductionSystemLoadEP2SMPEP2
38004New Year's Eve11131122013381110.684356.46149.776.020.4318.501097.04698.3866.12
38005New Year's Eve11131122013391136.224286.9574.846.022.2311.241085.04447.4262.05
38006New Year's Eve11131122013401160.574188.8566.085.018.5262.971143.04207.5762.05
38007New Year's Eve11131122013411183.584122.2665.395.020.4264.71941.04051.0159.48
38008New Year's Eve11131122013421204.714000.4963.325.018.5266.03913.03856.2050.60
38009New Year's Eve11131122013431179.143932.2234.516.022.2285.31812.03692.9542.45
38010New Year's Eve11131122013441152.013821.4433.835.024.1278.31852.03571.0033.83
38011New Year's Eve11131122013451123.673724.2131.754.020.4280.91962.03460.2931.75
38012New Year's Eve11131122013461094.243638.1633.835.014.8302.46950.03563.9950.60
38013New Year's Eve11131122013471064.003624.2533.835.016.7308.011020.03517.0834.90